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20 Best Practices of Power BI in 2024

03rd May, 2024
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    20 Best Practices of Power BI in 2024

    Power BI enables analysts and scientists to turn unorganized data into visually meaningful representations. To get the most out of Power BI, aspiring professionals must understand best practices. I'll walk you through each of the 20 Power BI best practices in 2023 with examples from the real world throughout this extensive document. Whether you're a college student exploring analytics or a newbie data analyst, mastering Power BI will skyrocket your career and make you a valuable asset. Power BI visualization best practices are also important when it comes to showcasing your reports to higher leadership who require only top-shelf information for quick decision-making. Power bi training is an essential toolkit that aspiring analysts should have in their array of skillsets.

    Power BI: Overview

    Microsoft's Power BI is an important resource for businesses looking into the complexities of visualization of information and making choices. Power BI shines as the preferred choice among professionals for converting raw data into useful knowledge because of its user-friendly interface and comprehensive features. Its fluid features enable the smooth sharing of information across groups, making it an essential asset for companies focused on improving their data-driven decision-making. In summary. Power BI development best practices and power BI data flow best practices are the two important elements that belong to the Power BI best practices handbook and should be kept in mind when preparing and publishing a report.

    20 Best Practices of Power BI

    1. Limit the visuals in dashboards and reports

    Do you know what slows the report performance? The Microsoft Power BI performance best practices highlight that placing many visuals in a single report is responsible for it. This is what you need to do in order to limit the number of visuals in dashboards and reports:

    • Limit to a minimum of eight widget visuals in every report page and keep the grids to a minimum of one in every page
    • The pages should be limited to no more than 30 points (cards: 1, gauges: 2, charts: 3, maps: 3, grids: 5)
    • Keep the tiles limited to no more than 10 per dashboard.

    2. Remove unnecessary interactions between visuals

    Want to know the secret of improving Power BI report performance? Here’s a hint! You can make that possible by removing unnecessary interactions between visuals. This is possible because of the reason that all visuals on a report can interact with one another by default. The interactivity should be controlled and modified for optimal performance.
    Further, you can reduce the number of queries fired at the backend and improve report performance by disabling unwanted interactivity.

    3. Enable Row-Level Security (RLS)

    Power BI only imports the data that the user is authorized to view, with RLS restricting user access to certain rows in a database depending on the characteristics of the user executing a query.
    But how to attain substantial performance gains? You can enable this by combining Power BI roles with roles in the backend. Moreover, you need to test all roles prior to rolling them out to production.

    4. Use Microsoft AppSource-certified custom visuals

    The Power BI-certified custom visuals are verified by Microsoft to have robust as well as well-performing code. These AppSource visuals have passed rigorous quality testing and are the only custom visuals that can be viewed in Export to PowerPoint and email subscriptions.

    5. Avoid using hierarchical filters

    Yes, that’s what you need to do when you observe poor performance in Power BI. Are you getting bothered by high page load times while using hierarchical filters? Try this! Remove the hierarchical filters. Experience an enhanced performance in Power BI by using multiple filters for the hierarchy.

    6. Categorize the data for Power BI reports

    One of Power BI's best practices is providing data categorization for the Power BI reports (HBI, MBI, LBI). The Power BI data classification enables you to raise user awareness about the security level that is required to be used. This also helps you understand how reports should be shared inside and outside the organization.

    The categories can be listed as follows:

    • HBI or High Business Impact data requires users to get a policy exception to share the data eternally.
    • LBI or Low Business Impact, as well as MBI or Medium Business Impact, do not require any exceptions.

    Categorize the data for Power BI reports
    7. Use the On-premises data gateway

    It is suggestible as well as one of the best practices to use an on-premises data gateway instead of a Personal Gateway, for it takes data and imports it into Power BI. But why Enterprise Gateway? It is more efficient while you work with large databases, as Enterprise Gateway imports nothing.

    8. Use separate Power BI gateways for “Direct Query” and “Scheduled Refresh”

    As you know, using the same gateway for Scheduled Data Refresh and Live Connection slows down the Live Connection performance when the Scheduled Data Refresh is active. You should create separate gateways for Live Connection and Scheduled Refresh to avoid such issues.

    9. Test each custom visual on a report to ensure fast report load time

    The Power BI team doesn’t thoroughly test the custom visuals that are not certified. So, while handling large datasets or complex aggregations, the custom visuals might perform poorly.
    What should you do when the chosen visual perform poorly? You can overcome the issue by using an alternative vision. Ensure fast report load time by testing each custom visual on a report for performance.

    10. Limit complicated measures and aggregations in data models

    Increase the likelihood of improved performance by pushing calculated columns and measures closer to the source wherever possible. Moreover, you need to create calculated measures instead of calculated columns while using star schema in order to design data models.

    11. Import what’s necessary

    Why do you need to import entire datasets when you can keep the model as narrow and lean as possible by importing only necessary fields? Power BI works on columnar indexes where longer and leaner are preferred.

    12. Reduce the Amount of Data Loaded on the Page

    Improve performance by reducing data that isn't needed load. For effective handling of huge data sets, use pagination or dynamic loading. Implementing effective data loading methods eliminates sluggish report performance and ensures a pleasant experience for users, especially when dealing with massive datasets. This is one of the most important best practices for effective Power BI usage.

    Use the edit query to apply required filters and load only the required data, this can make refresh faster and reduce load on the data platforms like Snowflake, MySQL, etc

    13.Using version control systems(VCS) in Power BI

    Version control is a great way for developers to work with Power BI, to use it follow the steps.
    Create a Git repository, a.gitignore file, and stage/commit changes  to use version control systems in Power BI. Connect to a remote repository to collaborate, push/pull changes, establish features/bug fixes branches, and tag releases. Enhance development and collaboration workflows by integrating version-controlled assets into Power BI Service . VCS come under power bi design best practices that improve the collaboration and tracking of changes from a dev point of view, as it provides a deeper control and history management.

    14. Choose Storage Mode for Tables Appropriately

    By selecting the right storage mode, you can significantly enhance query performance. For large fact tables, try using the "Import" option to improve performance.

    Power BI's Composite Data Model enables easy connection to multiple data sources. By merging imported and DirectQuery tables, this allows for large-scale data models to be prepared seamlessly. Selecting the best storage mode helps optimize data retrieval, improving query performance and overall efficacy when handling massive data sets. Storage selection is a crucial Power BI best practices element that needs to be followed with utmost importance.

    In my experience with large models which I use for calculating availability of items for quick commerce, we have mutliple tables and load them as composite data sources to integrate in Power BI  through defined relationships between them. Otherwise loading a huge table with 2M+ records would crash Power BI and make it unusable.

    15. Cross-Check Referential Integrity for Relationships

    Establishing well-defined relationships enables accurate analysis. Referential integrity checks ensure that data is consistent and reliable. Referential integrity checks ensure the accuracy and dependability of table relationships, ensuring the integrity of your results from analysis.

    For example, in an Ecom setup, a foreign key linking the "Orders" table to the "Customers" table is a possibilty. A referential integrity check guarantees that each order is associated with a real client, preventing data discrepancies.

    16. Ensure Reports and Data Sources Are in the Same Region

    By synchronizing reports and data sources, you can minimize delay. Hosting both in the same Azure region increases total report speed by optimizing data retrieval. Co-locating reports and data sources decreases transmission latency, making the reporting ecosystem quicker and more productive. Power BI best practices for datetime variables are often overlooked by many analysts, but as we can see they can improve the overall stability and reliability of the report.

    17. Use hierarchy to drill down and up the chart

    The usage of hierarchies in  Power BI enriches the user experience through the usage for drill-down and drill-up actions. In a time-based hierarchy, for example (Year > Quarter > Month), users may drill down from yearly to quarterly or monthly details to get deeper insights. Drill-up, on the other hand, allows the users to move back up the hierarchy. This hierarchical structure improves data exploration capabilities by simplifying navigation and analysis.

    At Instamart/Blinkit/Zepto like companies, it is important to look at current stock levels of campaign items from a city > store level across different times of the day, for example, before 12 PM, after 4PM and 10 PM closing stock, for this having a drill down chart can work wonders as the stakeholders can quickly identify stock outs and plan accordingly.

    18. Avoid Bi-Directional and Many-to-Many Relationships Against High Cardinality Columns

    Use relationships effectively for better performance. Consider different possibilities for complicated situations to save too much information processing. In complex analytical settings, thorough investigation of different kinds of connections and options helps to effective processing of info and greater efficiency. Relationship management comes under the Microsoft power bi performance best practices realm where the performance of the report is checked.

    A single one to one relationship is the best for indexers to work faster in backend( for example, capital city to country mapping), while bidirectional and many to many relationships( for example, countries to state names) can have a slight impact to the model depending on the cardinality of the dataset. More many to many relationships mean more query processing and hence overload at high cardinality situations.

    19. Avoid Using Floating Point Data Types

    Use whole numbers or decimals data types to avoid precision issues. Using the correct data types ensures reliable calculations. Using precision-appropriate data formats minimizes calculation mistakes, which produces reliable and precise analytical outputs.

    Precision in computations require the data to be stored in a certain way and in SQL, for optimal computation, there are different precision point datatypes like NUMBER(38,0) , NUMBER(38,5), DECIMAL, INTEGER etc.If you are sure that the value you are seeking is only a number without a decimal point, you can alter the dataset to only use INTEGER or NUMBER(38,0) types. The more suiting the datatype is, the better is the memory utilization and hence query performance. Efficient floating point number usage is one of the easier ways that I use to employ Power BI best practices when it comes to better visual and relevant points.

    20. Replace the Auto-Generated Date Table with a Custom Date Table in Your Model

    A custom date table gives you greater control over date-related operations. Include additional columns for fiscal quarters and holidays to meet certain requirements for reporting. Modifying a date table to meet unique reporting needs offers more freedom and accuracy in calendar-related calculations which as an analyst you will face in most cases.

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    In conclusion, understanding the best practices of power bi reveals a route to remarkable effectiveness and simplicity in data analytics. I've discovered the art of creating impactful reports by judiciously limiting data visualizations, streamlining data models, and ensuring security through Row-Level Security (RLS). Using Microsoft AppSource-certified visualizations and region-specific hosting improves Power BI's stability and efficiency even more. These methods, when paired with sensible gateway management and thorough evaluation of exclusive visuals, not only boost the consumer's experience but also reinforce the foundation for accurate and quick decision-making. Courses like KnowledgeHut Power BI training can be your best tool when it comes to learning these as a beginner or intermediate-level professional. Employing Microsoft power bi best practices is one of the important points that help professionals toward excellence in Power BI usage and building value for clients and cost optimizations for the company.


    Utpal Kar

    Blog Author

    Utpal Kar, a seasoned Corporate Trainer, excels in conducting training programs encompassing Advanced Excel, Power BI, Python, SQL Server, and Unix/Linux technologies. Notably, he holds a Python Certification from LinkedIn, showcasing his proficiency in the domain. Currently serving as a Corporate Trainer at Innovative Technology Solutions, Utpal specializes in Python, VBA Macro, Advance Excel, Power BI, and PostgreSQL, along with a breadth of other languages like .Net and Java. Prior to this role, he made significant contributions at NIIT Ltd., providing technical support and solutions to Franchisee Centres. With over 4 years at Innovative Technology Solutions, Utpal remains dedicated to enhancing skill sets and driving performance for professionals across various industries.

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